Calibrating a network of directional sensors may be achieved using a number of techniques. One known technique for calibrating a network of camera-based sensors requires a single, centralized processor which receives image feature data from each of the sensors and then performs a minimization of calibration errors for the entire network. This single processor method is accurate when calibrating small networks (e.g., having less than 50 sensors) but is not suitable when scaled to larger networks (e.g., having more than 50 sensors).
A localization algorithm method for calibrating camera networks is described in Lee et al., “Collaborative Self-Localization Techniques for Wireless Image Sensor Networks,” Proc. Asilomar Conf. on Signals, Sys., & Computers (2005), the entire disclosure of which is expressly incorporated by reference herein. This localization algorithm method basically estimates the location of a moving target and a camera node. The method selects two reference nodes to define an origin and a unit length. The method then finds the position and orientation of the sensors in the network with respect to the reference nodes. This localization algorithm method has been tested in two dimensional (2D) space using a very small network (i.e., five camera-based sensors).
A distributed localization algorithm method for calibrating camera networks is described in Mantzel et al., “Distributed Camera Network Localization,” Proc. Asilomar Conf. on Signals, Sys., & Computers (2004), the entire disclosure of which is expressly incorporated by reference herein. This distributed localization algorithm method iteratively refines the localization estimates for each camera in the network. The method assumes that the image features required for localization have already been acquired and that the correspondences between the image features are known. This distributed localization algorithm allows for localization of a static network, but will not operate in a dynamic network where the image features required for localization are not already present.
An automated calibration protocol method for calibrating camera networks is described in Liu et al., “A Self-Calibration Protocol for Camera Sensor Networks,” Technical Rep., Dept. Computer Sci., Univ. of Mass. (2005), the entire disclosure of which is expressly incorporated by reference herein. This automated calibration protocol method uses a calibration device that is equipped with a global positioning system (GPS) and a light emitting diode (LED). Camera position and orientation are calculated using data received from the calibration device and image coordinate data received from the camera. For each camera requiring calibration, the calibration device is placed in front of each camera by a user, and the data obtained from the device and the camera is used to calculate the position and orientation of the camera being calibrated.
A distributed calibration algorithm method for calibrating camera networks is described in Devarajan et al., “Distributed Metric Calibration of Large Camera Networks,” Proc. Int'l Conf. on Broadband Networks (2004), the entire disclosure of which is expressly incorporated by reference herein. This distributed calibration algorithm method assumes that communication between any two cameras is possible within the network. Communication may be made through a wired network where each camera can communicate with any other camera or through a wireless network where each camera can only communicate with any camera within a wireless network range. The wireless communication embodiment assumes that all cameras within the network are within the wireless communication range.
A simultaneous localization and tracking method for calibrating camera networks is described in Funiak et al., “Distributed Localization of Networked Camera,” Int'l Conf. on Info. Processing Sensor Networks 34-42 (2006), the entire disclosure of which is expressly incorporated by reference herein. This simultaneous localization and tracking method uses several ceiling mounted cameras to determine the positions of the cameras while determining the trajectory of a target object trajectory. This method considers only three extrinsic parameters (x, y, θ) of the camera. As the cameras are ceiling-mounted, the height above ground parameter (z) is essentially known by all cameras in the network prior to calibration
The aforementioned calibration methods all have limitations, such as requiring human interaction during the calibration process or imposing constraints on the positioning of the cameras to satisfy calibration algorithm requirements, by way of example. Furthermore, these methods each require either significant computing capabilities to deal with the necessary data and calculations and/or powerful communication infrastructures in which each camera may communicate with any other camera. In other words, these conventional methods generally suffer from the so-called scalability problem of camera calibration. As the number of cameras increases in the network, the communication network traffic increases significantly due to the exchange of more information between pairs of cameras or between the central server and the cameras. At the same time, the computational tasks required for camera calibration also increase significantly. In addition, whenever any local changes in camera configurations become necessary, conventional calibration methods typically required the revision of calibration parameters for the entire network. The conventional approaches described above do not provide communication and computational efficiencies for large camera networks to overcome these challenges. Thus, these conventional calibration methods can only practically deal with relatively small sizes of camera network (e.g., having less than 50 cameras).
Additional calibration techniques and background principles are described in R. Hartley et al., Multiple View Geometry in Computer Vision (2004); O. Faugeras, Three-Dimensional Computer Vision (1993); P. Baker et al. “Calibration of a Multicamera Network,” Proc. Omnidirectional Vision & Camera Networks (2003); S. Capkun et al., “GPS-Free Positioning in Mobile Ad-Hoc Networks,” 5 Cluster Computing 157-66 (2002); A. Galstyan et al. “Distributed Online Localization in Sensor Networks Using a Moving Target,” Proc. Int'l Symp. on Info. Processing Sensor Networks 61-70 (2004); R. Hartley, “In Defense of the Eight-Point Algorithm,” 19 IEEE Transactions on Pattern Analysis & Machine Intelligence 580-93 (1997); R. Iyengar et al., “Scalable and Distributed GPS Free Positioning for Sensor Networks,” Proc. IEEE Int'l Conf. on Comm. 338-42 (2003); M. Lourakis et al., “The Design and Implementation of a Generic Sparse Bundle Adjustment Software Package Based on the Levenberg-Marquardt Algorithm,” Technical Rep. 340, Inst. Computer Sci. —FORTH (2004); H. Medeiros et al., “Online Distributed Calibration of a Large Network of Wireless Cameras Using Dynamic Clustering,” ACM/IEEE Int'l Conf. on Distributed Smart Cameras (2008); M. Pollefeys et al., “Self-calibration and Metric Reconstruction in Spite of Varying and Unknown Intrinsic Camera Parameters,” 32 Int'l J. Computer Vision 7-25 (1999); M. Rahimi et al., “Cyclops: In Situ Image Sensing and Interpretation in Wireless Sensor Networks,” Proc. Int'l Conf. on Embedded Networked Sensor Sys. 192-204 (2005); C. Savarese et al., “Locationing in Distributed Ad-Hoc Wireless Sensor Networks,” Proc. IEEE Int'l Conf. on Acoustics, Speech, & Signal Processing 2037-40 (2001); A. Savvides et al., “Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors,” Proc. Int'l Conf. on Mobile Computing & Networking 166-79 (2001); T. Svoboda et al., “A Convenient Multi-Camera Self-Calibration for Virtual Environments,” 14 PRESENCE: Teleoperators & Virtual Env'ts 407-22 (2005); B. Triggs et al., “Bundle Adjustment—A Modern Synthesis,” Vision Algorithms: Theory & Practice, Lecture Notes Computer Sci. 153-77 (2000); J. Weng et al., “Motion and Structure from Two Perspective Views: Algorithms, Error Analysis, and Error Estimation,” 11 IEEE Transactions on Pattern Analysis & Machine Intelligence 451-76 (1989). The entire disclosures of each of the above listed references is expressly incorporated herein by reference. This listing is not intended as a representation that a complete search of all relevant prior art has been conducted or that no better reference than those listed above exist; nor should any such representation be inferred.